Performance of two Improved Particle Swarm Optimization In Dynamic Optimization Environments

The particle swarm optimization (PSO) was originally designed by Kennedy and Eberhart in 1995 and has been applied successfully in solving various optimization problems. The PSO idea is inspired by natural concepts such as fish schooling, bird flocking and human social relations. Two improved particle swarm optimization were introduced in this paper, which were swarm-core evolutionary particle swarm optimization (SCEPSO) and PSO with simulated annealing (PSOwSA). The performances of PSO, PSOwSA and SECPSO in dynamic environments were discussed, experiments for three type of dynamic optimization model imply that the SCEPSO can track a continuously changing solution reliably and accurately compare with PSO and PSOwSA